# Dolgopyat's method and the fractal uncertainty principle

## TL;DR

This paper establishes an improved fractal uncertainty principle for regular fractal sets, leveraging Dolgopyat-inspired techniques, with applications to spectral gaps in hyperbolic surfaces and quantum maps.

## Contribution

It introduces a sharper fractal uncertainty principle for regular fractals, extending Dolgopyat's methods beyond group invariance, with explicit epsilon estimates.

## Key findings

- Improved uncertainty principle exponent for fractal sets.
- Explicit epsilon estimates based on set regularity.
- New spectral gap results for hyperbolic surfaces and quantum maps.

## Abstract

We show a fractal uncertainty principle with exponent $1/2-\delta+\epsilon$, $\epsilon>0$, for Ahflors-David regular subsets of $\mathbb R$ of dimension $\delta\in (0,1)$. This improves over the volume bound $1/2-\delta$, and $\epsilon$ is estimated explicitly in terms of the regularity constant of the set. The proof uses a version of techniques originating in the works of Dolgopyat, Naud, and Stoyanov on spectral radii of transfer operators. Here the group invariance of the set is replaced by its fractal structure. As an application, we quantify the result of Naud on spectral gaps for convex co-compact hyperbolic surfaces and obtain a new spectral gap for open quantum baker maps.